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Modeling Syntactic-Semantic Dependency Correlations in Semantic Role Labeling Using Mixture Models

Junjie Chen, Xiangheng He, Yusuke Miyao
2022 Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)   unpublished
In this paper, we propose a mixture modelbased end-to-end method to model the syntactic-semantic dependency correlation in Semantic Role Labeling (SRL).  ...  We target the variation of semantic label distributions using a mixture model, separately estimating semantic label distributions for different hop patterns and probabilistically clustering hop patterns  ...  In this paper, we propose a mixture model (Pearson, 1894) based semantic dependency parser for SRL where we target the dependence of semantic label distributions on Shortest Syntactic Dependency Path (  ... 
doi:10.18653/v1/2022.acl-long.548 fatcat:gneq62ccr5djtdf5uupb56aopy

Computational linking theory [article]

Aaron Steven White, Drew Reisinger, Rachel Rudinger, Kyle Rawlins, Benjamin Van Durme
2016 arXiv   pre-print
A linking theory explains how verbs' semantic arguments are mapped to their syntactic arguments---the inverse of the Semantic Role Labeling task from the shallow semantic parsing literature.  ...  To further investigate the behavior of these models, we develop a measurement model in the spirit of previous work in semantic role induction: the Semantic Proto-Role Linking Model.  ...  Experiment 2 The sequence of syntactic positions in each clause was used as the dependent variable. For instance, (5) would be labeled {subj, obj, obl}.  ... 
arXiv:1610.02544v1 fatcat:6by23zv4nrflznusemjoln6b4i

The Study of Semantic Analysis on Intelligence Research under the Environment of Big Data

Hong Gu, Hongwei Yuan
2017 Modern Applied Science  
analysis technology in the application of intelligence research, exemplified by the semantic role annotation and semantic-based text orientation analysis of two methods, described the meaning of these  ...  two methods, the semantic database, the basic flow of information, their strengths and weaknesses, as well asdevelopment and raised its outlook in information research.  ...  labeling of semantic role labeling system is syntactic constituents, phrases, words or dependencies.  ... 
doi:10.5539/mas.v11n4p1 fatcat:n3e3ywu3avgebh4v7sibrcul2m

The Semantic Proto-Role Linking Model

Aaron Steven White, Kyle Rawlins, Benjamin Van Durme
2017 Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 2, Short Papers  
We use this model to empirically evaluate Dowty's thematic proto-role linking theory.  ...  We propose the semantic proto-role linking model, which jointly induces both predicate-specific semantic roles and predicate-general semantic proto-roles based on semantic proto-role property likelihood  ...  Acknowledgments This work was supported in part by the JHU HLT-COE and DARPA LORELEI. The U.S. Government is authorized to reproduce and distribute reprints for Governmental purposes.  ... 
doi:10.18653/v1/e17-2015 dblp:conf/eacl/DurmeWR17 fatcat:blasczcjwjd4fa7oepddxhu7cy

Spoken language understanding: a survey

Renato De Mori
2007 2007 IEEE Workshop on Automatic Speech Recognition & Understanding (ASRU)  
It covers aspects of knowledge representation, robust automatic interpretation strategies, semantic grammars, conceptual language models, semantic event detection, shallow semantic parsing, semantic classification  ...  , semantic confidence and active learning.  ...  PropBank is another project which adds a layer of predicate argument information or semantic role labels to the syntactic structures of the Penn Treebank.  ... 
doi:10.1109/asru.2007.4430139 dblp:conf/asru/Mori07 fatcat:vfi3o4yqtnh7pdflaolgkvptv4

Integrating Topics and Syntax

Thomas L. Griffiths, Mark Steyvers, David M. Blei, Joshua B. Tenenbaum
2004 Neural Information Processing Systems  
We present a generative model that uses both kinds of dependencies, and can be used to simultaneously find syntactic classes and semantic topics despite having no representation of syntax or semantics  ...  Statistical approaches to language learning typically focus on either short-range syntactic dependencies or long-range semantic dependencies between words.  ...  This model can be used to extract clean syntactic and semantic classes and to identify the role that words play in a document.  ... 
dblp:conf/nips/GriffithsSBT04 fatcat:q3zdlmw6o5doxhplxxcf3t2w6i

Spoken language understanding

R. De Mori, F. Bechet, D. Hakkani-Tur, M. McTear, G. Riccardi, G. Tur
2008 IEEE Signal Processing Magazine  
Systems developed in the 1970s and the 1980s mostly performed syntactic analysis on the best sequence of words hypothesized by an ASR system and used nonprobabilistic rules for mapping syntactic structures  ...  In the 1990s, the need emerged for testing SLU processes on large corpora that could also be used for automatically estimating some model parameters.  ...  role labels to the syntactic structures of the Penn Treebank.  ... 
doi:10.1109/msp.2008.918413 fatcat:zq6vxulkjrbwhpve7cmcgdb3mi

A Dataset for Semantic Role Labelling of Hindi-English Code-Mixed Tweets

Riya Pal, Dipti Sharma
2019 Proceedings of the 13th Linguistic Annotation Workshop  
While there is relevant ongoing research on Semantic Role Labelling (SRL) and on building tools for code-mixed social media data, this is the first attempt at labelling semantic roles in Hindi-English  ...  With the help of these mappings and the dependency tree, we propose a baseline rule based system for Semantic Role Labelling of Hindi-English code-mixed data.  ...  Dependency labels provide us with rich syntactic-semantic relations which facilitates mapping between dependency labels and Propbank labels.  ... 
doi:10.18653/v1/w19-4020 dblp:conf/acllaw/PalS19 fatcat:zxvc6rbxgvhflevyfquwfe6g2a

A Generative Model for Semantic Role Labeling [chapter]

Cynthia A. Thompson, Roger Levy, Christopher D. Manning
2003 Lecture Notes in Computer Science  
We train the model using the FrameNet corpus and apply it to the task of automatic semantic role and frame identification, producing results competitive with previous work (about 70% role labeling accuracy  ...  We present a model of natural language generation from semantics using the FrameNet semantic role and frame ontology.  ...  Acknowledgments This work was supported in part by the Advanced Research and Development Activity (ARDA)'s Advanced Question Answering for Intelligence (AQUAINT) Program, and in part by an IBM Faculty  ... 
doi:10.1007/978-3-540-39857-8_36 fatcat:diycm7cofncfdfn4tdjfp7ikfe

Identifying Locus of Control in Social Media Language

Masoud Rouhizadeh, Kokil Jaidka, Laura Smith, H. Andrew Schwartz, Anneke Buffone, Lyle Ungar
2018 Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing  
We explore the roles of syntax and semantics in expressing users' sense of control -i.e. being "controlled by" or "in control of" their circumstances-in a corpus of annotated Facebook posts.  ...  with lexical features outperforming syntactic features at the task.  ...  The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.  ... 
doi:10.18653/v1/d18-1145 dblp:conf/emnlp/RouhizadehJSSBU18 fatcat:zikykrbbtrhefccegdzvplvni4

Distributed Representations for Compositional Semantics [article]

Karl Moritz Hermann
2014 arXiv   pre-print
state-of-the-art models which apply distributed semantic representations to various tasks in NLP.  ...  Our underlying hypothesis is that neural models are a suitable vehicle for learning semantically rich representations and that such representations in turn are suitable vehicles for solving important tasks  ...  Stephen's encouragement was vital to getting me started again in research after my long detour away from computer science.  ... 
arXiv:1411.3146v1 fatcat:nvopl5a5afa6fdozfhxovhlhi4

DeSCoVeR: Debiased Semantic Context Prior for Venue Recommendation

Sailaja Rajanala, Arghya Pal, Manish Singh, Raphaël C.-W. Phan, KokSheik Wong
2022 Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval  
The proposed methodology that we call DeSCoVeR first elicits these semantic and syntactic features using a Neural Topic Model and text classifier, respectively.  ...  Based on the intuition that the text in the title and abstract have both semantic and syntactic components, we demonstrate that joint training of a semantic feature extractor and syntactic feature extractor  ...  We label a document's topics as its semantic features or conversely, its semantic context using the Neural Topic Model [26] with a variational semantic loss: L 𝑠𝑚 " min 𝜙 2 ,𝜃 2 E z 2 "𝑞 2 1p𝐵𝑜𝑊  ... 
doi:10.1145/3477495.3531877 fatcat:zjbbj4clufgsrmfic2bfk7bdfy

Bootstrapping the Syntactic Bootstrapper: Probabilistic Labeling of Prosodic Phrases

Ariel Gutman, Isabelle Dautriche, Benoît Crabbé, Anne Christophe
2014 Language Acquisition  
The models take as input a corpus of French child-directed speech tagged with prosodic boundaries and assign syntactic labels to prosodic phrases.  ...  , and a minimal semantic knowledge.  ...  ACKNOWLEDGMENTS This work originated in the first author's master thesis of the Master Parisien de Recherche en Informatique.  ... 
doi:10.1080/10489223.2014.971956 fatcat:iduitq3ufzbyffyx2bgxpjr3wq

Selectional Preferences for Semantic Role Classification

Beñat Zapirain, Eneko Agirre, Lluís Màrquez, Mihai Surdeanu
2013 Computational Linguistics  
Finally, we show that in an end-to-end semantic role labeling system we obtain small but statistically significant improvements, even though our modified SRC model affects only approximately 4% of the  ...  This paper focuses on a well-known open issue in Semantic Role Classification (SRC) research: the limited influence and sparseness of lexical features.  ...  in this revision.  ... 
doi:10.1162/coli_a_00145 fatcat:wadqjxjw7bcvvaay4f6agredgi

Knowledge discovery through directed probabilistic topic models: a survey

Ali Daud, Juanzi Li, Lizhu Zhou, Faqir Muhammad
2010 Frontiers of Computer Science in China  
In topic modeling, a document consists of different hidden topics and the topic probabilities provide an explicit representation of a document to smooth data from the semantic level.  ...  From an unsupervised learning perspective, "topics are semantically related probabilistic clusters of words in text corpora; and the process for finding these topics is called topic modeling".  ...  Especially we are thankful to Wim De Smet for helping us to improve English writing and anonymous reviewers for their valuable suggestions, which has really improved the contents and structure of the paper  ... 
doi:10.1007/s11704-009-0062-y fatcat:m6y7bayiwnccvfc5pfjl2ongh4
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